ragflow

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📕 Table of Contents

• 💡 What is RAGFlow?
• 🎮 Get Started
• 🔥 Latest Updates
• 🌟 Key Features
• 🔎 System Architecture
• 🎬 Self-Hosting
• 🔧 Configurations
• 🔧 Build a Docker Image
• 🔨 Launch Service from Source for Development
• 📚 Documentation
• 📜 Roadmap
• 🏄 Community
• 🙌 Contributing

💡 What is RAGFlow?

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.

🎮 Get Started

Try our cloud service at https://cloud.ragflow.io.

🔥 Latest Updates

• 2026-06-15 Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc.
• 2026-04-24 Supports DeepSeek v4.
• 2026-03-24 RAGFlow Skill on OpenClaw — Provides an official skill for accessing RAGFlow datasets via OpenClaw.
• 2025-12-26 Supports 'Memory' for AI agent.
• 2025-11-19 Supports Gemini 3 Pro.
• 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.
• 2025-10-23 Supports MinerU & Docling as document parsing methods.
• 2025-10-15 Supports orchestrable ingestion pipeline.
• 2025-08-08 Supports OpenAI's latest GPT-5 series models.
• 2025-08-01 Supports agentic workflow and MCP.
• 2025-05-23 Adds a Python/JavaScript code executor component to Agent.
• 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.

🎉 Stay Tuned

⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟

🌟 Key Features

🍭 "Quality in, quality out"

formats.

• Deep document understanding-based knowledge extraction from unstructured data with complicated
• Finds "needle in a data haystack" of literally unlimited tokens.

🍱 Template-based chunking

• Intelligent and explainable.
• Plenty of template options to choose from.

🌱 Grounded citations with reduced hallucinations

• Visualization of text chunking to allow human intervention.
• Quick view of the key references and traceable citations to support grounded answers.

🍔 Compatibility with heterogeneous data sources

• Supports Word, Slides, Excel, TXT, images, scanned copies, structured data, web pages, and more.

🛀 Automated and effortless RAG workflow

• Streamlined RAG orchestration catered to both personal and large businesses.
• Configurable LLMs as well as embedding models.
• Multiple recall paired with fused re-ranking.
• Intuitive APIs for seamless integration with business.

🔎 System Architecture

🎬 Self-Hosting

📝 Prerequisites

• CPU >= 4 cores
• RAM >= 16 GB
• Disk >= 50 GB
• Docker >= 24.0.0 & Docker Compose >= v2.26.1
• Python >= 3.13
• gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.

[!TIP]

If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

1. Ensure vm.maxmapcount >= 262144:

To check the value of vm.maxmapcount:

```bash

sysctl vm.maxmapcount

```

Reset vm.maxmapcount to a value at least 262144 if it is not.

```bash

# In this case, we set it to 262144:

sudo sysctl -w vm.maxmapcount=262144

```

This change will be reset after a system reboot. To ensure your change remains permanent, add or update the

vm.maxmapcount value in /etc/sysctl.conf accordingly:

```bash

vm.maxmapcount=262144

```

1. Clone the repo:

   git clone https://github.com/infiniflow/ragflow.git

1. Start up the server using the pre-built Docker images:

[!CAUTION]

All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.

If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.

The command below downloads the v0.27.1 edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.27.1, update the RAGFLOWIMAGE variable accordingly in docker/.env before using docker compose to start the server.

   cd ragflow/docker

   git checkout v0.27.1
   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
   # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.

   # Use CPU for DeepDoc tasks:
   docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate DeepDoc tasks:
   # sed -i '1i DEVICE=gpu' .env
   # docker compose -f docker-compose.yml up -d

Note: Prior to v0.22.0, we provided both images with embedding models and slim images without embedding models. Details as follows:

| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | |-------------------|-----------------|-----------------------|----------------| | v0.21.1 | ≈9 | ✔️ | Stable release | | v0.21.1-slim | ≈2 | ❌ | Stable release |

Starting with v0.22.0, we ship only the slim edition and no longer append the -slim suffix to the image tag.

1. Check the server status after having the server up and running:

   docker logs -f docker-ragflow-cpu-1

The following output confirms a successful launch of the system:

         ____   ___    ______ ______ __
        / __ \ /   |  / ____// ____// /____  _      __
       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/

    * Running on all addresses (0.0.0.0)

If you skip this confirmation step and direc